{"id":"sinanuozdemir-oreilly-ai-agents","name":"oreilly-ai-agents","af_score":34.8,"security_score":25.0,"reliability_score":17.5,"what_it_does":"A collection of educational notebooks and example code for learning how to build and deploy AI agents (single-agent and multi-agent patterns) using popular Python frameworks (e.g., LangChain/LangGraph, CrewAI, AutoGen, SmolAgents) and integrating tools such as MCP, along with evaluation-oriented notebooks.","best_when":"As a learning repository to study patterns and adapt notebook code into your own agent implementations.","avoid_when":"If you need a well-defined API/SDK with versioned interfaces, strict security boundaries, or guaranteed idempotent/retry-safe operations; also avoid running notebook code you don’t fully understand—especially those that enable local machine interaction.","last_evaluated":"2026-03-30T13:35:10.071464+00:00","has_mcp":false,"has_api":false,"auth_methods":[],"has_free_tier":false,"known_gotchas":["Educational notebooks may contain non-production patterns (e.g., lack of robust retry/idempotency controls)","Some notebooks explicitly warn that AI code can use the local machine (GUI automation/computer-use), which is a major safety risk if run in an uncontrolled environment","Tool-selection/agent routing may exhibit model-dependent positional biases (noted in the repository content)"],"error_quality":0.0}